This page follows FastGPT v4.17.0 configuration guidelines.
What data for this category looks like
Data sources for this category include government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. Documents processed daily cover policy documents, policy interpretation drafts, public directories, information release approval forms, press releases, and website column update records. Some documents contain multi-level policy association information, such as superior document references and supporting implementation rules.
Data is updated daily, and must be synced to multiple platform channels. Documents include fields such as document number, issuing authority, release time, public attribute, and secrecy level. Some documents include image and text attachments and associated interpretation information.
What constraints do these characteristics impose on the vector model and indexing link?
Daily high-frequency updated data requires indexing to support incremental updates, avoiding excessively long full rebuild times. Large differences in document structure across types require adapted vector extraction logic for different fields, preventing non-policy text from interfering with retrieval results. The presence of public attribute and secrecy fields requires adding metadata filtering rules during the indexing link, preventing sensitive information from entering the retrieval scope. Data formats from multiple sources are inconsistent, requiring standardized cleaning before indexing to ensure consistent vector generation. Additionally, the long paragraph characteristics of policy texts require reasonable control of chunking and overlap parameters. The defaultToken value must be adjusted based on document length to fit the input requirements of vector models, avoiding semantic breaks or redundant matching.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the typical paragraph length of policy documents, avoiding semantic breaks and redundant vector generation |
| Overlap Length | 100–200 characters | Retains logical associations across paragraphs of policy text, improving retrieval context completeness |
maxToken | 8192 | Fits the input upper limit of mainstream vector models, avoiding truncation of core policy content |
| Similarity Threshold | 0.75–0.85 | Matches the semantic similarity characteristics of policy texts, filtering low-correlation retrieval results |
| Single Search Limit | Top 10 entries | Controls the number of retrieval results, simplifying subsequent context splicing and review processes |
| Metadata Filtering | Filter by public attribute and secrecy level | Associates document metadata, excluding content that should not be disclosed from the retrieval scope |
Three common errors
- Phenomenon: After performing an index switch, retrieval results remain unchanged for a long time or return old data. Cause: The automatic sync task for the old index was not disabled, or the incremental index configuration was not bound to the latest data source path.
- Phenomenon: A parameter error is returned when adding indexes in bulk, and index ingestion cannot be completed. Cause: Required document metadata fields were not included, or the request body format did not match the structure required by the system.
- Phenomenon: Retrieval results return multiple chunk entries from a single document, or duplicate matching policy files appear. Cause: No unified index deduplication rules were configured, or the chunk overlap length was set too large, leading to semantic duplicate matching.
How to confirm configurations are properly set
- A test policy document is uploaded, and index generation logs are reviewed to confirm chunk length and overlap length align with preset configurations.
- A simulated policy query request is initiated, and metadata filtering in retrieval results is checked to exclude confidential or non-public documents.
- An incremental index sync is performed, and the number of retrieval results before and after sync is compared to confirm the update logic is functioning correctly.
- Multiple documents of the same type are uploaded in bulk, and the completeness of index ingestion is checked to confirm no indexing failures caused by format errors.
The values provided are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.